Overview

Dataset statistics

Number of variables18
Number of observations10646
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory362.7 B

Variable types

Categorical6
Numeric12

Alerts

FileName has a high cardinality: 10646 distinct values High cardinality
Beat has a high cardinality: 742 distinct values High cardinality
VentricularRate is highly correlated with AtrialRate and 4 other fieldsHigh correlation
AtrialRate is highly correlated with VentricularRate and 3 other fieldsHigh correlation
QRSDuration is highly correlated with QOffsetHigh correlation
QTInterval is highly correlated with VentricularRate and 3 other fieldsHigh correlation
QTCorrected is highly correlated with VentricularRate and 1 other fieldsHigh correlation
QRSCount is highly correlated with VentricularRate and 4 other fieldsHigh correlation
QOffset is highly correlated with QRSDurationHigh correlation
TOffset is highly correlated with VentricularRate and 3 other fieldsHigh correlation
Gender_FEMALE is highly correlated with Gender_MALEHigh correlation
Gender_MALE is highly correlated with Gender_FEMALEHigh correlation
VentricularRate is highly correlated with AtrialRate and 3 other fieldsHigh correlation
AtrialRate is highly correlated with VentricularRate and 1 other fieldsHigh correlation
QRSDuration is highly correlated with QOffsetHigh correlation
QTInterval is highly correlated with VentricularRate and 2 other fieldsHigh correlation
QRSCount is highly correlated with VentricularRate and 3 other fieldsHigh correlation
QOffset is highly correlated with QRSDurationHigh correlation
TOffset is highly correlated with VentricularRate and 2 other fieldsHigh correlation
Gender_FEMALE is highly correlated with Gender_MALEHigh correlation
Gender_MALE is highly correlated with Gender_FEMALEHigh correlation
VentricularRate is highly correlated with AtrialRate and 3 other fieldsHigh correlation
AtrialRate is highly correlated with VentricularRate and 3 other fieldsHigh correlation
QTInterval is highly correlated with VentricularRate and 3 other fieldsHigh correlation
QRSCount is highly correlated with VentricularRate and 3 other fieldsHigh correlation
TOffset is highly correlated with VentricularRate and 3 other fieldsHigh correlation
Gender_FEMALE is highly correlated with Gender_MALEHigh correlation
Gender_MALE is highly correlated with Gender_FEMALEHigh correlation
Gender_MALE is highly correlated with Gender_FEMALEHigh correlation
Rhythm is highly correlated with Rhythm_groupedHigh correlation
Gender_FEMALE is highly correlated with Gender_MALEHigh correlation
Rhythm_grouped is highly correlated with RhythmHigh correlation
Rhythm is highly correlated with VentricularRate and 5 other fieldsHigh correlation
VentricularRate is highly correlated with Rhythm and 6 other fieldsHigh correlation
AtrialRate is highly correlated with Rhythm and 5 other fieldsHigh correlation
QRSDuration is highly correlated with QTCorrected and 2 other fieldsHigh correlation
QTInterval is highly correlated with Rhythm and 6 other fieldsHigh correlation
QTCorrected is highly correlated with VentricularRate and 4 other fieldsHigh correlation
QRSCount is highly correlated with Rhythm and 6 other fieldsHigh correlation
QOnset is highly correlated with QRSDurationHigh correlation
QOffset is highly correlated with QRSDurationHigh correlation
TOffset is highly correlated with Rhythm and 6 other fieldsHigh correlation
Rhythm_grouped is highly correlated with Rhythm and 5 other fieldsHigh correlation
Gender_FEMALE is highly correlated with Gender_MALEHigh correlation
Gender_MALE is highly correlated with Gender_FEMALEHigh correlation
FileName is uniformly distributed Uniform
FileName has unique values Unique

Reproduction

Analysis started2021-10-13 17:39:00.596636
Analysis finished2021-10-13 17:39:20.711242
Duration20.11 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

FileName
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct10646
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size863.0 KiB
MUSE_20180113_171327_27000
 
1
MUSE_20180712_154810_09000
 
1
MUSE_20180114_125325_47000
 
1
MUSE_20180113_135515_20000
 
1
MUSE_20180210_123445_64000
 
1
Other values (10641)
10641 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10646 ?
Unique (%)100.0%

Sample

1st rowMUSE_20180113_171327_27000
2nd rowMUSE_20180112_073319_29000
3rd rowMUSE_20180111_165520_97000
4th rowMUSE_20180113_121940_44000
5th rowMUSE_20180112_122850_57000

Common Values

ValueCountFrequency (%)
MUSE_20180113_171327_270001
 
< 0.1%
MUSE_20180712_154810_090001
 
< 0.1%
MUSE_20180114_125325_470001
 
< 0.1%
MUSE_20180113_135515_200001
 
< 0.1%
MUSE_20180210_123445_640001
 
< 0.1%
MUSE_20180113_132809_550001
 
< 0.1%
MUSE_20180116_181417_620001
 
< 0.1%
MUSE_20180115_133059_090001
 
< 0.1%
MUSE_20180119_170619_360001
 
< 0.1%
MUSE_20180112_073033_150001
 
< 0.1%
Other values (10636)10636
99.9%

Length

2021-10-13T14:39:20.865451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
muse_20180113_171327_270001
 
< 0.1%
muse_20180116_123940_900001
 
< 0.1%
muse_20180115_132518_010001
 
< 0.1%
muse_20180114_122918_820001
 
< 0.1%
muse_20180111_165520_970001
 
< 0.1%
muse_20180113_121940_440001
 
< 0.1%
muse_20180112_122850_570001
 
< 0.1%
muse_20180112_120347_790001
 
< 0.1%
muse_20180114_075026_690001
 
< 0.1%
muse_20180209_172046_210001
 
< 0.1%
Other values (10636)10636
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Rhythm
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size617.7 KiB
SB
3889 
SR
1826 
AFIB
1780 
ST
1568 
SVT
587 
Other values (6)
996 

Length

Max length5
Median length2
Mean length2.397520195
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFIB
2nd rowSB
3rd rowSA
4th rowSB
5th rowAF

Common Values

ValueCountFrequency (%)
SB3889
36.5%
SR1826
17.2%
AFIB1780
16.7%
ST1568
14.7%
SVT587
 
5.5%
AF445
 
4.2%
SA399
 
3.7%
AT121
 
1.1%
AVNRT16
 
0.2%
AVRT8
 
0.1%

Length

2021-10-13T14:39:20.956381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sb3889
36.5%
sr1826
17.2%
afib1780
16.7%
st1568
14.7%
svt587
 
5.5%
af445
 
4.2%
sa399
 
3.7%
at121
 
1.1%
avnrt16
 
0.2%
avrt8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Beat
Categorical

HIGH CARDINALITY

Distinct742
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size648.2 KiB
NONE
5419 
TWC
775 
LVHV
577 
STTC
 
429
RBBB
 
189
Other values (737)
3257 

Length

Max length39
Median length4
Mean length5.339000564
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique458 ?
Unique (%)4.3%

Sample

1st rowRBBB TWC
2nd rowTWC
3rd rowNONE
4th rowNONE
5th rowSTDD STTC

Common Values

ValueCountFrequency (%)
NONE5419
50.9%
TWC775
 
7.3%
LVHV577
 
5.4%
STTC429
 
4.0%
RBBB189
 
1.8%
LVHV TWC178
 
1.7%
ALS130
 
1.2%
LVHV STTC123
 
1.2%
STDD STTC TWC105
 
1.0%
1AVB103
 
1.0%
Other values (732)2618
24.6%

Length

2021-10-13T14:39:21.077376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none5419
38.1%
twc1906
 
13.4%
lvhv1314
 
9.2%
sttc1170
 
8.2%
rbbb460
 
3.2%
stdd406
 
2.9%
als385
 
2.7%
vpb310
 
2.2%
apb278
 
2.0%
1avb252
 
1.8%
Other values (47)2313
16.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PatientAge
Real number (ℝ≥0)

Distinct95
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.18683073
Minimum4
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:21.214346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile25
Q149
median62
Q372
95-th percentile85
Maximum98
Range94
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.03001879
Coefficient of variation (CV)0.3046288941
Kurtosis0.09198212253
Mean59.18683073
Median Absolute Deviation (MAD)11
Skewness-0.6110209811
Sum630103
Variance325.0815776
MonotonicityNot monotonic
2021-10-13T14:39:21.359780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69309
 
2.9%
67281
 
2.6%
68277
 
2.6%
63274
 
2.6%
64271
 
2.5%
60258
 
2.4%
71251
 
2.4%
65247
 
2.3%
61247
 
2.3%
62244
 
2.3%
Other values (85)7987
75.0%
ValueCountFrequency (%)
413
0.1%
526
0.2%
615
0.1%
716
0.2%
820
0.2%
926
0.2%
1020
0.2%
1117
0.2%
1214
0.1%
1316
0.2%
ValueCountFrequency (%)
982
 
< 0.1%
973
 
< 0.1%
965
 
< 0.1%
957
 
0.1%
9424
 
0.2%
9315
 
0.1%
9235
0.3%
9140
0.4%
9037
0.3%
8968
0.6%

VentricularRate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct189
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.13610746
Minimum34
Maximum263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:21.456699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile50
Q157
median73
Q3104
95-th percentile157
Maximum263
Range229
Interquartile range (IQR)47

Descriptive statistics

Standard deviation34.45534858
Coefficient of variation (CV)0.4095191662
Kurtosis1.368993575
Mean84.13610746
Median Absolute Deviation (MAD)18
Skewness1.294592488
Sum895713
Variance1187.171046
MonotonicityNot monotonic
2021-10-13T14:39:21.588131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59608
 
5.7%
58585
 
5.5%
57537
 
5.0%
56446
 
4.2%
55376
 
3.5%
54293
 
2.8%
53267
 
2.5%
52199
 
1.9%
102164
 
1.5%
101153
 
1.4%
Other values (179)7018
65.9%
ValueCountFrequency (%)
342
 
< 0.1%
353
 
< 0.1%
372
 
< 0.1%
392
 
< 0.1%
407
 
0.1%
4112
 
0.1%
429
 
0.1%
4319
0.2%
4425
0.2%
4541
0.4%
ValueCountFrequency (%)
2631
< 0.1%
2441
< 0.1%
2411
< 0.1%
2381
< 0.1%
2301
< 0.1%
2281
< 0.1%
2251
< 0.1%
2222
< 0.1%
2202
< 0.1%
2181
< 0.1%

AtrialRate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.57786962
Minimum0
Maximum535
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:21.719208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q157
median72
Q3106
95-th percentile200.75
Maximum535
Range535
Interquartile range (IQR)49

Descriptive statistics

Standard deviation60.09394004
Coefficient of variation (CV)0.6491177674
Kurtosis11.5120293
Mean92.57786962
Median Absolute Deviation (MAD)18
Skewness3.018680766
Sum985584
Variance3611.28163
MonotonicityNot monotonic
2021-10-13T14:39:21.974432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59606
 
5.7%
58592
 
5.6%
57548
 
5.1%
56452
 
4.2%
55379
 
3.6%
54290
 
2.7%
53266
 
2.5%
52212
 
2.0%
101156
 
1.5%
51152
 
1.4%
Other values (243)6993
65.7%
ValueCountFrequency (%)
012
0.1%
181
 
< 0.1%
202
 
< 0.1%
221
 
< 0.1%
232
 
< 0.1%
242
 
< 0.1%
251
 
< 0.1%
262
 
< 0.1%
271
 
< 0.1%
283
 
< 0.1%
ValueCountFrequency (%)
5351
 
< 0.1%
5008
 
0.1%
4689
 
0.1%
44118
0.2%
41626
0.2%
3962
 
< 0.1%
39424
0.2%
3811
 
< 0.1%
37515
0.1%
3661
 
< 0.1%

QRSDuration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct80
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.98929175
Minimum18
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:22.116715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile70
Q180
median88
Q398
95-th percentile124
Maximum256
Range238
Interquartile range (IQR)18

Descriptive statistics

Standard deviation17.47953989
Coefficient of variation (CV)0.1921054615
Kurtosis7.531064262
Mean90.98929175
Median Absolute Deviation (MAD)8
Skewness2.021912096
Sum968672
Variance305.5343146
MonotonicityNot monotonic
2021-10-13T14:39:22.237240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82715
 
6.7%
88690
 
6.5%
84687
 
6.5%
86676
 
6.3%
80647
 
6.1%
90601
 
5.6%
78567
 
5.3%
92564
 
5.3%
94540
 
5.1%
76491
 
4.6%
Other values (70)4468
42.0%
ValueCountFrequency (%)
181
 
< 0.1%
522
 
< 0.1%
564
 
< 0.1%
5811
 
0.1%
6019
 
0.2%
6234
 
0.3%
6458
 
0.5%
6688
0.8%
68148
1.4%
70215
2.0%
ValueCountFrequency (%)
2561
< 0.1%
2342
< 0.1%
2321
< 0.1%
2182
< 0.1%
2161
< 0.1%
2101
< 0.1%
2082
< 0.1%
2061
< 0.1%
2022
< 0.1%
2001
< 0.1%

QTInterval
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct206
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean384.1508548
Minimum114
Maximum736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:22.372570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile280
Q1344
median390
Q3426
95-th percentile470
Maximum736
Range622
Interquartile range (IQR)82

Descriptive statistics

Standard deviation59.22628881
Coefficient of variation (CV)0.1541745595
Kurtosis0.57756984
Mean384.1508548
Median Absolute Deviation (MAD)40
Skewness-0.2080659055
Sum4089670
Variance3507.753286
MonotonicityNot monotonic
2021-10-13T14:39:22.515373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416178
 
1.7%
402164
 
1.5%
420163
 
1.5%
426159
 
1.5%
394158
 
1.5%
408155
 
1.5%
410155
 
1.5%
412154
 
1.4%
414153
 
1.4%
422151
 
1.4%
Other values (196)9056
85.1%
ValueCountFrequency (%)
1141
 
< 0.1%
1481
 
< 0.1%
1561
 
< 0.1%
1641
 
< 0.1%
1722
 
< 0.1%
1761
 
< 0.1%
1922
 
< 0.1%
1985
< 0.1%
2002
 
< 0.1%
2023
< 0.1%
ValueCountFrequency (%)
7361
< 0.1%
6941
< 0.1%
6882
< 0.1%
6801
< 0.1%
6681
< 0.1%
6521
< 0.1%
6481
< 0.1%
6401
< 0.1%
6341
< 0.1%
6161
< 0.1%

QTCorrected
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct295
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean434.1540485
Minimum219
Maximum760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:22.637769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum219
5-th percentile380
Q1408
median430
Q3456
95-th percentile502
Maximum760
Range541
Interquartile range (IQR)48

Descriptive statistics

Standard deviation39.35418217
Coefficient of variation (CV)0.09064566439
Kurtosis3.030518256
Mean434.1540485
Median Absolute Deviation (MAD)24
Skewness0.9125967803
Sum4622004
Variance1548.751654
MonotonicityNot monotonic
2021-10-13T14:39:22.783274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
414151
 
1.4%
422151
 
1.4%
435150
 
1.4%
441146
 
1.4%
418145
 
1.4%
428145
 
1.4%
424140
 
1.3%
430139
 
1.3%
420139
 
1.3%
401133
 
1.2%
Other values (285)9207
86.5%
ValueCountFrequency (%)
2191
< 0.1%
2241
< 0.1%
2401
< 0.1%
2691
< 0.1%
2701
< 0.1%
2771
< 0.1%
2831
< 0.1%
2901
< 0.1%
3021
< 0.1%
3031
< 0.1%
ValueCountFrequency (%)
7601
 
< 0.1%
6991
 
< 0.1%
6701
 
< 0.1%
6691
 
< 0.1%
6581
 
< 0.1%
6503
< 0.1%
6441
 
< 0.1%
6431
 
< 0.1%
6292
< 0.1%
6261
 
< 0.1%

RAxis
Real number (ℝ)

Distinct277
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.62060868
Minimum-89
Maximum270
Zeros82
Zeros (%)0.8%
Negative1726
Negative (%)16.2%
Memory size83.3 KiB
2021-10-13T14:39:22.934460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile-35
Q114
median46
Q368
95-th percentile89
Maximum270
Range359
Interquartile range (IQR)54

Descriptive statistics

Standard deviation41.21264796
Coefficient of variation (CV)1.040182101
Kurtosis2.579873155
Mean39.62060868
Median Absolute Deviation (MAD)26
Skewness0.02772669064
Sum421801
Variance1698.482352
MonotonicityNot monotonic
2021-10-13T14:39:23.086664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64144
 
1.4%
67137
 
1.3%
66136
 
1.3%
52135
 
1.3%
63133
 
1.2%
71133
 
1.2%
70132
 
1.2%
72132
 
1.2%
65132
 
1.2%
68130
 
1.2%
Other values (267)9302
87.4%
ValueCountFrequency (%)
-894
< 0.1%
-882
 
< 0.1%
-872
 
< 0.1%
-861
 
< 0.1%
-854
< 0.1%
-845
< 0.1%
-836
0.1%
-824
< 0.1%
-813
< 0.1%
-802
 
< 0.1%
ValueCountFrequency (%)
2701
 
< 0.1%
2692
< 0.1%
2681
 
< 0.1%
2671
 
< 0.1%
2662
< 0.1%
2641
 
< 0.1%
2632
< 0.1%
2621
 
< 0.1%
2611
 
< 0.1%
2594
< 0.1%

TAxis
Real number (ℝ)

Distinct356
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.6377043
Minimum-89
Maximum270
Zeros52
Zeros (%)0.5%
Negative1473
Negative (%)13.8%
Memory size83.3 KiB
2021-10-13T14:39:23.228257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile-44
Q119
median42
Q362
95-th percentile168
Maximum270
Range359
Interquartile range (IQR)43

Descriptive statistics

Standard deviation57.4780924
Coefficient of variation (CV)1.287657896
Kurtosis4.149231872
Mean44.6377043
Median Absolute Deviation (MAD)21
Skewness1.381912279
Sum475213
Variance3303.731106
MonotonicityNot monotonic
2021-10-13T14:39:23.348209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50160
 
1.5%
35158
 
1.5%
43151
 
1.4%
36150
 
1.4%
59150
 
1.4%
39147
 
1.4%
44144
 
1.4%
52141
 
1.3%
55138
 
1.3%
53138
 
1.3%
Other values (346)9169
86.1%
ValueCountFrequency (%)
-898
0.1%
-885
 
< 0.1%
-878
0.1%
-8612
0.1%
-8512
0.1%
-8412
0.1%
-8310
0.1%
-826
0.1%
-8114
0.1%
-8010
0.1%
ValueCountFrequency (%)
2708
0.1%
2699
0.1%
26812
0.1%
2679
0.1%
26612
0.1%
2657
0.1%
2645
< 0.1%
2638
0.1%
2625
< 0.1%
2617
0.1%

QRSCount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.83947022
Minimum5
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:23.456742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q19
median12
Q317
95-th percentile26
Maximum40
Range35
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.676338821
Coefficient of variation (CV)0.4101557885
Kurtosis1.339844549
Mean13.83947022
Median Absolute Deviation (MAD)3
Skewness1.284146768
Sum147335
Variance32.22082242
MonotonicityNot monotonic
2021-10-13T14:39:23.569884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
92148
20.2%
101393
13.1%
12703
 
6.6%
17702
 
6.6%
11675
 
6.3%
8647
 
6.1%
13603
 
5.7%
18511
 
4.8%
14473
 
4.4%
15406
 
3.8%
Other values (25)2385
22.4%
ValueCountFrequency (%)
52
 
< 0.1%
614
 
0.1%
7121
 
1.1%
8647
 
6.1%
92148
20.2%
101393
13.1%
11675
 
6.3%
12703
 
6.6%
13603
 
5.7%
14473
 
4.4%
ValueCountFrequency (%)
404
 
< 0.1%
381
 
< 0.1%
372
 
< 0.1%
369
 
0.1%
3515
 
0.1%
3418
 
0.2%
3328
 
0.3%
3227
 
0.3%
3156
0.5%
3073
0.7%

QOnset
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.2605673
Minimum159
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:23.693280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum159
5-th percentile209
Q1216
median219
Q3224
95-th percentile228
Maximum240
Range81
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.410412987
Coefficient of variation (CV)0.02923650643
Kurtosis10.53205258
Mean219.2605673
Median Absolute Deviation (MAD)4
Skewness-1.61152252
Sum2334248
Variance41.09339466
MonotonicityNot monotonic
2021-10-13T14:39:23.834230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219844
 
7.9%
218811
 
7.6%
220731
 
6.9%
221686
 
6.4%
217634
 
6.0%
222546
 
5.1%
223538
 
5.1%
216525
 
4.9%
226497
 
4.7%
227488
 
4.6%
Other values (52)4346
40.8%
ValueCountFrequency (%)
1599
0.1%
1601
 
< 0.1%
1671
 
< 0.1%
1701
 
< 0.1%
1711
 
< 0.1%
1732
 
< 0.1%
1744
< 0.1%
1762
 
< 0.1%
1772
 
< 0.1%
1781
 
< 0.1%
ValueCountFrequency (%)
2401
 
< 0.1%
2351
 
< 0.1%
2343
 
< 0.1%
2338
 
0.1%
23214
 
0.1%
23156
 
0.5%
230150
 
1.4%
229240
2.3%
228351
3.3%
227488
4.6%

QOffset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.7552132
Minimum249
Maximum331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:23.957062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum249
5-th percentile254
Q1259
median264
Q3268
95-th percentile279.75
Maximum331
Range82
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.362119544
Coefficient of variation (CV)0.0315843433
Kurtosis4.550991894
Mean264.7552132
Median Absolute Deviation (MAD)4
Skewness1.568845557
Sum2818584
Variance69.92504328
MonotonicityNot monotonic
2021-10-13T14:39:24.071688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
262687
 
6.5%
263633
 
5.9%
261628
 
5.9%
264626
 
5.9%
260611
 
5.7%
266588
 
5.5%
265574
 
5.4%
259530
 
5.0%
267512
 
4.8%
258495
 
4.6%
Other values (58)4762
44.7%
ValueCountFrequency (%)
24938
 
0.4%
25036
 
0.3%
25165
 
0.6%
252106
 
1.0%
253181
 
1.7%
254214
2.0%
255301
2.8%
256309
2.9%
257449
4.2%
258495
4.6%
ValueCountFrequency (%)
3311
 
< 0.1%
3201
 
< 0.1%
3161
 
< 0.1%
3142
< 0.1%
3131
 
< 0.1%
3122
< 0.1%
3112
< 0.1%
3091
 
< 0.1%
3081
 
< 0.1%
3074
< 0.1%

TOffset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct206
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.3359947
Minimum281
Maximum582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2021-10-13T14:39:24.202255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum281
5-th percentile359
Q1392
median414
Q3432
95-th percentile455
Maximum582
Range301
Interquartile range (IQR)40

Descriptive statistics

Standard deviation29.77740363
Coefficient of variation (CV)0.07239192292
Kurtosis0.4523974964
Mean411.3359947
Median Absolute Deviation (MAD)20
Skewness-0.2128567141
Sum4379083
Variance886.6937667
MonotonicityNot monotonic
2021-10-13T14:39:24.304998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
424174
 
1.6%
428171
 
1.6%
422170
 
1.6%
419169
 
1.6%
429159
 
1.5%
417159
 
1.5%
427157
 
1.5%
423152
 
1.4%
425150
 
1.4%
431149
 
1.4%
Other values (196)9036
84.9%
ValueCountFrequency (%)
2811
 
< 0.1%
3001
 
< 0.1%
3021
 
< 0.1%
3071
 
< 0.1%
3101
 
< 0.1%
3111
 
< 0.1%
3121
 
< 0.1%
3142
< 0.1%
3152
< 0.1%
3163
< 0.1%
ValueCountFrequency (%)
5821
< 0.1%
5641
< 0.1%
5621
< 0.1%
5601
< 0.1%
5591
< 0.1%
5471
< 0.1%
5411
< 0.1%
5391
< 0.1%
5371
< 0.1%
5231
< 0.1%

Rhythm_grouped
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size623.1 KiB
SB
3889 
GSVT
2706 
AFIB
2225 
SR
1826 

Length

Max length4
Median length2
Mean length2.926357317
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFIB
2nd rowSB
3rd rowGSVT
4th rowSB
5th rowAFIB

Common Values

ValueCountFrequency (%)
SB3889
36.5%
GSVT2706
25.4%
AFIB2225
20.9%
SR1826
17.2%

Length

2021-10-13T14:39:24.592140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-13T14:39:24.656364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
sb3889
36.5%
gsvt2706
25.4%
afib2225
20.9%
sr1826
17.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Gender_FEMALE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size603.1 KiB
0
5956 
1
4690 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
05956
55.9%
14690
44.1%

Length

2021-10-13T14:39:24.730176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-13T14:39:24.800472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
05956
55.9%
14690
44.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Gender_MALE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size603.1 KiB
1
5956 
0
4690 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
15956
55.9%
04690
44.1%

Length

2021-10-13T14:39:24.854414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-13T14:39:24.920416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
15956
55.9%
04690
44.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-13T14:39:18.712252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:02.556352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.864900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.187666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:06.691934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.019655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.365189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:10.875575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.244147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:13.637711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:15.230002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.245705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:18.848248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:02.670722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.960899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.298604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:06.812693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.108785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.471838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:10.984595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.362484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:13.885856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:15.365098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.347355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:18.956783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:02.762122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.067573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.405285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:06.890857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.225183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.592589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:11.096586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.459857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.002994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:15.525067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.450540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:19.069266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:02.889698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.176532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.508495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.009924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.345935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.704294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:11.212859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.582482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.117149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:15.667078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.559116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:19.180956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:02.989668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.272714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.607539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.111303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.449064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.805733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:11.327263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.694546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.223804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:15.798193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.657132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:19.289349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.102149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.381749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.711971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.219509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.561233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.927931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:11.449086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.815464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.337896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:15.950823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.757400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:19.402323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.206911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.495879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.827611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.327410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.671462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:10.175640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:11.565452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.917689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.441819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:16.113910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.858676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:19.539360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.307654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.602931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.949159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.442080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.778708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:10.293411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:11.675632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:13.027819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.559228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:16.264874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.989075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:19.712103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.422302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.721007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:06.089537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.562773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:08.897530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:10.406598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:11.789566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:13.147947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.697633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:16.384560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:18.090082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:19.834288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.530130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.839774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:06.356550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.692849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.020539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:10.529425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:11.894270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:13.295763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.814382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:16.785263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:18.340749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:19.960293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.641038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:04.950006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:06.470205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.794741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.144907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:10.652280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.021890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:13.408801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:14.939790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.005283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:18.458885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:20.056318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:03.738432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:05.048405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:06.582282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:07.904406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:09.241618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:10.765885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:12.134037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:13.516042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:15.072084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:17.126717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-13T14:39:18.572987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-10-13T14:39:24.989009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-13T14:39:25.170429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-13T14:39:25.353892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-13T14:39:25.504727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-13T14:39:25.639041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-13T14:39:20.271084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-13T14:39:20.556497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FileNameRhythmBeatPatientAgeVentricularRateAtrialRateQRSDurationQTIntervalQTCorrectedRAxisTAxisQRSCountQOnsetQOffsetTOffsetRhythm_groupedGender_FEMALEGender_MALE
0MUSE_20180113_171327_27000AFIBRBBB TWC8511723411435649681-2719208265386AFIB01
1MUSE_20180112_073319_29000SBTWC5952529243240176428215261431SB10
2MUSE_20180111_165520_97000SANONE20676782382403882011224265415GSVT10
3MUSE_20180113_121940_44000SBNONE665353964564273439219267447SB01
4MUSE_20180112_122850_57000AFSTDD STTC7316216211425241368-4026228285354AFIB10
5MUSE_20180112_120347_79000SBNONE4657577040439338249225260427SB10
6MUSE_20180114_075026_69000AFIBTWC80988674360459698317215252395AFIB10
7MUSE_20180209_172046_21000SRNONE46636390376384243811221266409SR01
8MUSE_20180114_075128_92000SBNONE45595984390386786810218260413SB01
9MUSE_20180118_174026_42000SBNONE47585880420412804810212252422SB10

Last rows

FileNameRhythmBeatPatientAgeVentricularRateAtrialRateQRSDurationQTIntervalQTCorrectedRAxisTAxisQRSCountQOnsetQOffsetTOffsetRhythm_groupedGender_FEMALEGender_MALE
10636MUSE_20181222_204246_47000SVTVPE57184023435461917814430159276336GSVT01
10637MUSE_20181222_204248_77000SVTNONE231811597425243769-130228265354GSVT10
10638MUSE_20181222_204249_88000SVTNONE691781701223085305722529203264357GSVT01
10639MUSE_20181222_204302_49000SVTNONE36220220110224428-777936212267324GSVT01
10640MUSE_20181222_204303_61000SVTLVHV STTU TWC3615115188274434671225218262355GSVT01
10641MUSE_20181222_204306_99000SVTNONE801967316828451325824432177261319GSVT10
10642MUSE_20181222_204309_22000SVTNONE8116281162294482110-7527173254320GSVT10
10643MUSE_20181222_204310_31000SVTNONE39152921523405402503825208284378GSVT01
10644MUSE_20181222_204312_58000SVTNONE7617517812831052998-8329205269360GSVT01
10645MUSE_20181222_204314_78000SVTNONE7511710414031243526314419208278364GSVT01